Summary

This data set provides Vegetation Photosynthesis Respiration Model (VPRM) net ecosystem exchange (NEE) parameter values optimized to 65 flux tower sites across North America. The parameters include the basal rate of ecosystem respiration (beta), the slope of respiration with respect to temperature (alpha), light-use efficiency (LUE) (lambda), and LUE curve half-saturation (PAR_0). Observed eddy covariance data from the 65 tower sites, locally observed temperature and photosynthetically active radiation (PAR) along with satellite-derived phenology and moisture were used as input data to optimize the VPRM parameters for the 65 sites. The data are provided by individual site, plant functional types (PFTs), and all sites together, and as monthly, annual, and all available data. The data are for the conterminous USA, Alaska, and Canada for the period 2000 to 2006.

The 65 observation sites cover nine of the 17 PFTs of the International Geosphere-Biosphere Programme (IGBP) land cover classification scheme (Loveland and Belward, 1997). MODIS Collection 5 data were used for site phenology, land surface water, enhanced vegetation index, and land surface cover type.

There are nine data files with this data set in comma-separated (*.csv) format. The files provide monthly and annual data for individual sites, all sites, and sites grouped by PFT.

Table of Contents

Data Set Overview

Vegetation Photosynthesis Respiration Model (VPRM) is a simple diagnostic terrestrial flux model. An overview of the model structure is provided in Section 5 of this document. VPRM calculates net ecosystem exchange (NEE) using locally observed temperature and photosynthetically active radiation (PAR) along with satellite-derived phenology and moisture. This study examined VPRM parameters optimized to 65 observed data from flux towers across North America (FLUXNET 2007 data set), locally observed temperature and PAR along with satellite-derived phenology and moisture as input data. The parameters include: β- basal rate of ecosystem respiration, α- the slope of respiration with respect to temperature, λ- light-use efficiency (LUE), and PAR0-LUE curve half-saturation. Three temporal and three spatial windows for sum of squared errors minimization were examined – in time: monthly, annual, and all available data; and, in space: individual sites, sites grouped by PFT, and all sites together.

Project: North American Carbon Program (NACP)

The North American Carbon Program (NACP) is a multidisciplinary research program designed to improve scientific understanding of North America's carbon sources and sinks and of changes in carbon stocks needed to meet societal concerns and to provide tools for decision makers.

Study Area (All latitudes and longitudes are given in decimal degrees)

Site

Westernmost Longitude

Easternmost Longitude

Northernmost Latitude

Southernmost Latitude

North America: Conterminous US, Alaska, and Canada

-156.63

-68.74

71.31999

28.46

Table 1. The 65 North American flux tower sites used to parameterize VPRM. PFTs (land cover) are taken from the International Geosphere-Biosphere Programme (IGBP) land cover classification scheme (Loveland and Belward, 1997) or investigator descriptions where available, and otherwise derived from MODIS 1 km land surface classifications (Hilton et al., 2014). In the data files with "pft" in the file names, PFT corresponds to the land cover in the table below.

Site

Land cover (PFT)

Site name

Latitude

Longitude

CA-Ca1

1 – Evergreen Needleleaf Forest

British Columbia – Campbell River – Mature Forest Site

49.87

-125.534

CA-ca2

1 – Evergreen Needleleaf Forest

British Columbia – Campbell River –Clearcut site

49.87

-125.29

CA-ca3

1 – Evergreen Needleleaf Forest

British Columbia – Campbell River –Young Plantation site

49.87

-124.9

CA-Gro

5 – Mixed Forest

Ontario – Groundhog River-Mature Boreal Mixed Wood

48.22

-82.16

CA-Let

10 – Grasslands

Lethbridge

49.71

-112.94

CA-Mer

1 – Permanent Wetlands

Eastern Peatland – Mer Bleu

45.1

-75.52

CA-NS2

1 – Evergreen Needleleaf Fores

UCI-1930 burn site

55.91

-98.52

CA-NS3

1 – Evergreen Needleleaf Fores

UCI-1964 burn site

55.91

-98.38

CA-NS4

1 – Evergreen Needleleaf Fores

UCI-1964 burn site

55.91

-98.38

CA-NS5

1 – Evergreen Needleleaf Fores

UCI-1981 burn site

55.86

-98.49

CA-NS6

1 – Evergreen Needleleaf Fores

UCI-19389 burn site

55.92

-98.96

CA-NS7

1 – Evergreen Needleleaf Fores

UCI-1998 burn site

56.64

-99.95

CA-Oas

4 – Deciduous Broadleaf Forest

Sask – SSA Old Aspen

53.63

-106.2

CA-Obs

1 – Evergreen Needleleaf Forest

Sask – SSA Old Black Spruce

53.99

-105.12

CA-Ojp

1 – Evergreen Needleleaf Forest

Sask – SSA Old Jack Pine

53.92

-104.69

CA-Qcu

7 – Open Shrubland

Quebec Boreal Cutover Site

49.27

-74.04

CA-Qfo

1 – Evergreen Needleleaf Forest

Quebec Mature Boreal Forest Site

49.69

-74.34

CA-SF2

6 – Closed Shrublands

Sask – Fire 1989

54.25

-105.88

CA-SF3

6 – Closed Shrublands

Sask – Fire 1998

54.09

106.01

CA-SJ1

1 – Evergreen Needleleaf Forest

Sask – 1994 Harv. Jack Pine

53.91

-104.66

CA-SJ2

1 – Evergreen Needleleaf Forest

Sask – 2002 Harvested Jack Pin

53.95

-104.65

CA-WP1

11 – Permanent Wetlands

Western Peatland – LaBiche-Black Spruce/Larch Fen

54.96

-112.46

US-ARM

12 – Cropland

ARM Southern Great Plains site – Lamont – Oklahoma

36.61

-97.49

US-Atq

11 – Permanent Wetlands

Atqasuk – Alaska

70.47

-157.41

US-Aud

10 – Grasslands

Audubon Research Ranch – Arizona

31.59

-110.51

US-Blo

1 – Evergreen Needleleaf Forest

Blodgett Forest – California

38.9

-120.36

US-Bn1

1 – Evergreen Needleleaf Forest

Delta Junction 1920 Control site

63.92

-145.37

US-Bn2

4 – Deciduous Broadleaf Fores

Delta Junction 1987 Burn site

63.92

-145.37

US-Bn3

7 – Open Shrublands

Delta Junction 1999 Burn site

63.92

-145.74

US-Bo1

12 – Croplands

Bondville – Illinois

40.01

-88.29

US-Bo2

12 – Croplands

Bondville – Illinois (companion site)

40.01

-88.29

US-Brw

11 – Permanent Wetlands

Barrow – Alaska

71.32

-156.63

US-CaV

10 – Grasslands

Canaan Valley – West Virginia

36.06

-79.42

US-Dk1

10 – Grasslands

Duke Forest-open field – North Carolina

35.97

-79.09

US-Dk2

4 – Deciduous Broadleaf Forest

Duke Forest-hardwoods – North Carolina

35.97

-79.1

US-Dk3

1 – Evergreen Needleleaf Fores

Duke Forest – loblolly pine – North Carolina

35.98

-79.09

US-FPe

10 – Grasslands

Fort Peck – Montana

48.31

-105.1

US-Goo

10 – Grasslands

Goodwin Creek- Mississippi

34.25

-89.97

US-Ha1

4 – Deciduous Broadleaf Forest

Harvard Forest EMS Tower – Massachusetts (HFR1

42.54

-72.17

US-Ha2

1 – Evergreen Needleleaf Forest

Harvard Forest Hemlock Site – Massachusetts

42.54

-72.17

US-Ho1

1 – Evergreen Needleleaf Forest

Howland Forest (main tower) – Maine

45.2

-68.74

US-Ho2

1 – Evergreen Needleleaf Forest

Howland Forest (west tower) – Maine

45.21

-68.75

US-KS1

1 – Evergreen Needleleaf Forest

Florida-Kennedy Space Center (slash pine)

28.46

-80.67

US-KS2

6 – Closed Shrublands

Florida-Kennedy Space Center (scrub oak)

28.61

-80.67

US-Los

6 – Closed Shrublands

Lost Creek – Wisconsin

46.08

-89.98

US-Me2

1 – Evergreen Needleleaf Forest

Metolius-intermediate aged ponderosa pine – Oregon

44.55

-121.56

US-Me4

1 – Evergreen Needleleaf Forest

Metolius-old aged ponderosa pine – Oregon

44.5

-121.62

US-MMS

4 – Deciduous Broadleaf Forest

Morgan Monroe State Forest – Indiana

39.32

-86.41

US-MOz

4 – Deciduous Broadleaf Forest

Missouri Ozark Site

38.74

-92.2

US-Ne1

12 – Croplands

Mead – irrigated continuous maize site – Nebraska

41.1

-96.29

US-Ne2

12 – Croplands

Mead – irrigated maize-soybean rotation site – Nebraska

41.1

-96.28

US-Ne3

12 – Croplands

Mead – rainfed maize-soybean rotation site – Nebraska

41.18

-96.44

US-NR1

1 – Evergreen Needleleaf Forest

Niwot Ridge Forest – Colorado (LTER NWT1)

40.03

-105.55

US-PFa

5 – Mixed Fores

Park Falls/WLEF – Wisconsin

45.95

-90.27

US-SO2

6 – Closed Shrublands

Sky Oaks – Old Stand – California

33.37

-116.62

US-SO3

6 – Closed Shrublands

Sky Oaks – Young Stand – California

33.38

-116.62

US-SO4

6 – Closed Shrublands

Sky Oaks – California

33.37

-116.62

US-SP1

1 – Evergreen Needleleaf Fores

Slashpine-Austin Cary – 65 yr nat regen-FL

29.74

-82.22

US-SP2

1 – Evergreen Needleleaf Fores

Slashpine-Mize-clearcut-3 yr-regen-FL

29.76

-82.24

US-SP3

1 – Evergreen Needleleaf Fores

Slashpine-Donaldson-mid-rot – 12 yr-FL

29.75

-82.16

US-Syv

5 – Mixed Forest

Sylvania Wilderness Area – Michigan

46.24

-89.35

US-Ton

8 – Woody Savannas

Tonzi Ranch – California

38.43

-120.97

US-UMB

4 – Deciduous Broadleaf Forest

Univ. of Mich. Biological Station – Michigan

45.56

-84.71

US-Var

10 – Grasslands

Vaira Ranch – Ione – California

38.41

-120.95

US-WCr

4 – Deciduous Broadleaf Forest

Willow Creek – Wisconsin

45.81

-90.08

Data File Information

There are nine data files in comma-separated (.csv) format with this data set. The files provide VPRM parameter values optimized to 65 flux tower sites across North America. The data are for the time period 2000-2007. Not all files include the entire period. Only one file includes data for 2007. There are no missing values.

Table 2. File names and descriptions

File name

Description

Number of observations in each file

VPRM_parameters_all_sites_all_data.csv

This file provides parameter values for all years combined and site (site = "all")

1

VPRM_parameters_all_sites_annual_data.csv

This file provides yearly data for the years 2000 – 2007 for each of the four parameters and site (site = "all")

8

VPRM_parameters_all_sites_monthly_data.csv

This file provides parameter values for each month for each year for 2000 – 2006 and site (site = "all")

85

VPRM_parameters_individual_sites_all_data.csv

This file provides yearly parameter values for each of the 65 sites and “site”, which is the Fluxnet site ID. Note that there is not data for each year for each site. There is one year of data for each site. The data are for the period 2000 – 2004 where data are provided for some sites for the year 2000 and for other sites for 2001, etc.

65

VPRM_parameters_individual_sites_annual_data.csv

This file provides parameter values for each of the 65 sites by year for the period 2000 – 2006. Note that data are not provided for every year for each site. This file also includes “site” which is the Fluxnet site ID.

286

VPRM_parameters_individual_sites_monthly_data.csv

This file provides parameter values for each of the 65 sites for each month for the years 2000 – 2006. Note that data are not provided for every year for each site. This file also includes “site” which is the Fluxnet site ID.

3,432

VPRM_parameters_pft_sites_all_data.csv

This file provides parameter values by nine PFT classes for the year 2000 only.

9

VPRM_parameters_pft_sites_annual_data.csv

This file provides parameter values by nine PFT classes for each year for 2000 – 2006

61

VPRM_parameters_pft_sites_monthly_data.csv

This file provides parameter values by nine PFT classes for each month for each year for 2000 – 2006.

732

Table 3. Parameters in the data files

Site

This is provided in the files named VPRM_parameters_individual_sites__. This is the FLUXNETsite ID. It is also provided in the files named VPRM_parameter_values_all_sites, and the value is "all"

PFT

This is only in files named VPRM_parameters_pft_sites__. This is the land cover classification number as provided by Hilton et al., 2013. Refer to Table 1.

tstamp

Time stamp. Refers to the year or date of the provided data, in yyyy or yyyy-mm-dd.

lambda

Maximum light use efficiency

alpha

Slope of respiration with respect to temperature

beta

Basal respiration rate

PAR_0

LUE half-saturation value (PAR0 in the model)

Application and Derivation

Our findings are relevant to both land surface model upscaling as well as atmospheric inversion studies.

Quality Assessment

The structural simplicity of VPRM allows parameter estimations to be conducted that use many thousands of model evaluations. However, caution is advised against attempting site-specific ecological interpretation of short-term fluctuations in VPRM parameter values, fluxes, and residuals.

Data Acquisition, Materials, and Methods

VPRM calculates net ecosystem exchange (NEE) using locally observed temperature and photosynthetically active radiation (PAR) along with satellite-derived phenology and moisture. Temperature and PAR can come from site-level observations when modeling a point. VPRM does not consider driver data or flux results from previous time steps.

VPRM data inputs

Observed input data were from 65 flux towers across North America. These data are part of the 2007 FLUXNET Synthesis data set (http://www.fluxdata.org). For each site, this data set contains CO2 flux, air temperature, and PAR observations at 30 min intervals, as well as many other quantities not needed for VPRM. The 65 observation sites cover nine of the 17 PFTs of the International Geosphere-Biosphere Programme (IGBP) land cover classification scheme (Loveland and Belward, 1997): evergreen needleleaf forest (27 sites), deciduous broadleaf forest (8 sites), mixed forest (3 sites), closed shrublands (7 sites), open shrublands (2 sites), woody savannas (1 site), grasslands (7 sites), permanent wetlands (4 sites), and croplands (6 sites).

MODIS Collection 5 data were used for site phenology, land surface water, enhanced vegetation index, and land surface cover type:

Site phenology is from data set M*D12Q2 (Strahler et al., 1999a)

Reflectances are from data set M*D43A4 (Strahler et al., 1999b)

Vegetation indices are from data set M*D13A2 (Huete et al., 2002, and 1999)

The “*” in M*D is either “O”, representing the data from the Terra satellite, or “Y” representing the data from the Aqua satellite. Only MODIS data of “best” quality, as indicated by each MODIS product’s associated quality assurance flags were considered (Hilton et al., 2013).

The time period examined was 2000 to 2006, bounded in 2000 by the MODIS instrument launch and in 2006 by eddy covariance flux availability.

The VPRM model

VPRM is a simple diagnostic terrestrial flux model. VPRM models net ecosystem exchange (NEE) as the sum of a photosynthetic component (gross ecosystem exchange, GEE) and an ecosystem respiration component. An overview of the model structure followed by the definitions of the four parameters studied and included with this data set are provided below (refer to Hilton et al., 2013 and 2014).

Pscale, Wscale, and Tscale are dimensionless scaling terms. They each take values between 0.0 and 1.0 and are defined as follows:

Pscale is satellite derived and describes the impacts of leaf expansion and senescence on canopy-scale photosynthesis. Pscale is defined as 0.0 outside of the growing season, 1.0 during growing season peak greenness, and is a linear function of the satellite-derived land surface water index (LSWI) (Xiao et al., 2004) during leaf expansion and senescence.

(2) Pscale = 1+LSWI/2

Wscale is satellite derived and describes canopy moisture, and is defined as:

(3) Wscale = 1+LSWI/1+LSWImax

with LSWImax the growing-season maximum LSWI value. The value of the third dimensionless scaling term Tscale is taken from literature and describes the relationship between photosynthesis and temperature.

Tscale is defined as:

(4) Tscale = (T-Tmin) (T-Tmax)/ (T-Tmin) (T-Tmax) – (T-Topt)2

Pscale, Wscale, and Tscale by definition, vary in both time and space (Mahadevan et al., 2008). Respiration (R) is modeled as a linear function of observed surface air temperature (T):

(5) R = α·T+β

with user-supplied parameters α and β. NEE is the difference between the photosynthetic flux and the respiration flux:

(6) NEE = R - GEE

Four estimated parameters:

Within equations (1), (5), and (6), are the four estimated parameters provided in this data set:

λ governs the slope of the light-response curve (the relationship between photosynthetic CO2 flux and PAR)

α defines the slope of the respiration response to temperature

PAR0 defines a half saturation value for photosynthesis. That is, it specifies a PAR value at which further increases in PAR no longer enhance photosynthesis, as other limiting factors become dominant.

β thus specifies a minimal level of respiration that occurs regardless of air temperature.

The source code used to estimate these parameter values is available at https://github.com/Timothy-W-Hilton/VPRMLandSfcModel.

NEE varies on a number of different time scales (e.g.,daily, annual) and space scales (e.g., local land use and PFT heterogeneity, larger regions that experience similar climate patterns). An ideal land surface model parameter estimation method would allow parameter values to vary at space and time scales matching the ecological variations in NEE. Optimizing parameter values in short time intervals and small spatial windows would run the risk of overfitting as well as incur unnecessary computational cost. In this study, three temporal and three spatial windows for the sum of squared errors (SSE; we define VPRM residuals as NEEVPRM minus NEEobserved) minimization were examined:

in time: monthly, annual, and all available data

in space: individual sites, sites grouped by PFT, and all sites together.

This approach resulted in nine different parameter sets.

To search for parameter values that minimized SSE, differential evolution (DE) (Price et al., 2006) was used. DE is a genetic optimization algorithm that is both fast and more reliable in identifying a global optimum compared to gradient-based minimization algorithms. The DEoptim package (Ardia and Mullen, 2009) for the R language and platform for statistical computing (R Development Core Team, 2007) was used.

The Markov chain Monte Carlo (MCMC) optimization approach offers the advantage of delivering probability density functions (PDFs), and thus parameter uncertainties, rather than the single optimal value estimates provided by DE. Because of the substantially increased computational expense and the lack of a statistically robust residual likelihood function, DE was chosen to obtain the point estimates provided (Hilton et al., 2013).

VPRM NEE residual spatial structure

Semivariograms were used to evaluate residual spatial structure. Because VPRM NEE residuals are simply the difference between NEEobs and NEEVPRM, the spatial behaviors of these three quantities are interrelated.